The mesoscale heavy rainfall, “Senjo-Kousuitai” (SK), often causes severe disasters. This study utilizes a regional reanalysis dataset for Japan (RRJ-Conv; hourly data from 1976 to 2020 with horizontal resolution of 5 km) to extract and analyze SK events across Japan and its surrounding area. By applying an objective extraction method, we identified 6760 SK events. They are mostly oriented southwest–northeast or west–east and more frequently appear between June and October and in the morning. These characteristics of their occurrences agree with those in observations. While RRJ-Conv does not reproduce all observed SK events, the vast number of SK samples is useful for statistical analysis of their characteristics and environmental conditions. The present study demonstrates that the SK orientations have the strongest correlation with wind directions at 600 hPa with a slight clockwise deviation from the wind direction at this altitude. The environmental winds exhibit veering, whose magnitude decreases after onsets of SK.

 This study statistically investigated the occurrence environment of the Muroto Line, a south–north oriented orographic quasi-stationary convective band (QSCB) that appears from the Muroto Peninsula in the eastern part of Shikoku, Japan. We objectively identified the Muroto Lines that occurred between 2004 and 2022. We focused on cases dominated by relatively shallow (Low-top case, 12 cases) and deep convective clouds (High-top case, 6 cases) and compared their occurrence environments. The results revealed similarities and differences in the occurrence environments of orographic QSCBs at different convective cloud depths.

 In all cases, tropical cyclones (TCs) were located within 1500 km west of the Muroto Lines. The Low-top cases occurred between the TC and the North Pacific Subtropical High (NPSH), which was favorable for southerly geostrophic winds, whereas the peripheral flow of the NPSH prevailed in Shikoku during the High-top cases. In both types, horizontal wind direction ranged from east-southeasterly to south-southeasterly near the surface and southerly above 1.5 km in height, with warm-moist and conditionally unstable thermodynamic environment. Compared to the High-top cases, the Low-top cases occurred under more conditionally stable atmosphere, higher low- and mid-level wind speeds, larger low-level water vapor flux, and stronger vertical wind shear.

Super-resolution (SR) in deep learning is a technique to generate high-resolution (HR) outputs from low-resolution (LR) inputs. Recently, combining SR with data assimilation (DA) has been proposed, leading to the development of super-resolution data assimilation (SRDA). The SRDA method simultaneously performs SR and DA by inputting LR simulation results and observations into a neural network. This study develops a four-dimensional SRDA (4D-SRDA) model to predict temporal evolutions of three-dimensional quasi-geostrophic flows in a baroclinic jet system. To evaluate the performance of 4D-SRDA, we compare it with a Local Ensemble Transform Kalman Filter (LETKF), which uses an HR model. 4D-SRDA successfully reproduces both small- and large-scale structures of potential vorticity, visually similar to those produced by the LETKF.We compare grid-wise and pattern-similarity errors to quantify the accuracy of the analysis and forecast states. Despite using an LR fluid model, 4D-SRDA achieves accuracy comparable to that of the LETKF. Comparing the computational time required for prediction reveals that 4D-SRDA is substantially more efficient than the LETKF. These results suggest that 4D-SRDA is a promising approach for predicting HR atmospheric flows.

This study introduces an object-based approach for assessing the prediction accuracy of quasi-stationary band-shaped precipitation systems (QSBPSs), known as “Senjo-Kousuitai.” Unlike a conventional grid-based verification approach, the proposed method integrates the unique characteristics of QSBPSs, including their shape, size, and duration, to provide a comprehensive evaluation of forecast accuracy. Quantitative prediction verification was performed by analyzing the features of QSBPSs based on the error between the observation and numerical weather prediction using the operational forecast model. The proposed approach was applied to Japan and its surrounding areas from June to September 2024, during which 41 QSBPSs cases were identified based on observational data. A total of 157 QSBPSs cases, including multiple forecasts of the same observed events, were detected using the local forecast model operated by the Japan Meteorological Agency with 2 km horizontal resolution and 18-hour forecasts eight times daily. The proposed approach was applied to these observed and predicted cases to evaluate prediction accuracy. In addition, a new index, the “SCS: Senjo-Kousuitai (QSBPSs) Composite Score,” was introduced to evaluate forecast performance across the entire period. The preliminary results obtained by applying this method indicate its effectiveness in quantitatively assessing the accuracy of numerical weather predictions for QSBPSs.

Typhoons AMPIL and SHANSHAN (2024) slowed down as they approached and moved parallel to the Kuroshio Current. Both typhoons reached peak intensity at relatively high latitudes while they were moving northward. Various atmospheric and oceanic datasets were used to investigate atmospheric and oceanic thermodynamic similarities and differences between AMPIL and SHANSHAN. In addition, numerical simulations and sensitivity experiments for high sea surface temperature in the Kuroshio Current region were performed with a nonhydrostatic atmosphere model and an atmosphere-wave-ocean coupled model to understand related intensification mechanism. The 26°C isotherm was relatively deep and the upper-ocean heat content was relatively high where AMPIL and SHANSHAN intensified. Despite the different atmospheric environments, such as the vertical wind shear and the relative humidity at the 600-hPa altitude, AMPIL and SHANSHAN were able to develop and maintain the maximum intensity at relatively high latitudes during when the TCs moved slowly near the Kuroshio Current region. The results of the sensitivity experiments suggest that high SSTs in the Kuroshio Current region possibly contribute to the development and maintenance of the maximum intensity of AMPIL and SHANSHAN through high latent heat fluxes outside the radius of maximum wind speed and the inner-core axisymmetrization.

In 2017/18 winter, the Siberian High intensified significantly, leading to a severe cold winter in central Eurasia. Here, we apply the event attribution methods using two types of historical large-ensemble simulations from an atmospheric general circulation model to quantify the influence of human activities on this event. The 2017/18 winter was dominated by a circulation regime known as the Warm-Arctic Cold-Eurasia (WACE) pattern, and both observation and model showed high WACE indices. However, the models exhibited a bias characterized by weak magnitudes of the cold Eurasian anomalies associated with the WACE, attributed to an underrepresentation of externally driven components. Anthropogenic climate change has increased the probability of the positive WACE regime year by year, significantly enhancing its occurrence in the 2017/18 winter. However, influences of this modulation of WACE occurrence did not significantly alter the probability of cold events in central Eurasia due to the model bias characterized by the muted cold lobe associated with the WACE. While a simple bias correction resolved this issue, it was demonstrated that the presence or absence of such corrections led to vastly different attribution results. Elucidating the mechanisms behind the WACE and accurately representing them in models is essential for more reliable attribution.

 To estimate global frozen precipitation particle characteristics, this study developed a method that utilizes Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observations. This method estimates the volume-weighted mean diameter before melting (D_m) and the number concentration (N_w) of frozen precipitation particles for a given particle model from the DPR reflectivity factors (Z_e). The likelihood of this particle model is estimated using the difference between the brightness temperatures at 89 and 166 GHz calculated from this D_m and N_w (TB_c) and the GMI observation (TB_o) as a measure.

 Particle models representing snowflake, aggregate, strongly rimed aggregate, and graupel (sphere) were selected from existing scattering databases. Under idealized conditions, the TB_c computed from DPR Z_e was highly dependent on the particle model. In the OLYMPEX (December 3, 2015) case, the TB_c calculated from the observed DPR Z_e also depended on the particle model. The most likely particle models were spherical particles and strong rimed aggregates south of the Olympic Mountains and aggregates to the north. The D_m values for the most likely particle models had a smaller bias than the D_m values for each particle model when compared with the OLYMPEX airborne observations.